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1:29

It Stephen Dubner. Before we get

1:31

to today's episode, I want to ask

1:34

for your help for a special series

1:36

we're just starting to make. It is

1:38

about mentorship and this is where you

1:41

come in. We're looking for some

1:43

good stories. It could be about a

1:45

mentorship in business or academia or and

1:47

sports. It could be a spiritual mentor

1:50

or someone to help you become a

1:52

better parent spouse. Or maybe you are

1:54

the mentor. Or maybe you have a

1:57

mentor who doesn't even know they

1:59

are. You are meant. No

2:01

relationship is too small or too weird.

2:03

If it matters to you, send us

2:05

an email with some of the particulars.

2:08

We're. At Radio at Freakonomics That

2:10

com We look forward to reading

2:12

your stories and interviewing some of

2:15

you for the series. Thanks in

2:17

advance! And now today's episode. Here's

2:23

a phrase you have probably heard

2:25

before: The road to Hell is

2:28

paved with good intentions. The sentiment

2:30

goes back at least to the

2:32

bible, but the weights use today

2:34

likely began with the eighteenth century

2:37

writer Samuel Johnson. Since then, versions

2:39

of the phrase have appeared in

2:41

the works of Charlotte Bronte and

2:43

Lord Byron, Soren, Kierkegaard and Karl

2:46

Marx, Ozzie Osborne and Madonna. Ah,

2:58

but how would an economist

3:00

think about it, I would

3:02

say, economics is fundamentally about

3:04

trade offs and they're always

3:06

treat? Today on

3:09

freaking I'm thirty three stories about

3:11

good intentions gone bad in the

3:13

workplace is anything if made them

3:16

worse off by reducing their employment

3:18

rates. I find that Jr seem

3:20

like to them expat few a new

3:22

potter. But can economists

3:25

have turned good intentions into

3:27

good outcomes? I. Can't emphasize

3:29

enough that the slight adjustments he

3:31

can get your cake and eat

3:33

it too. Yes, he should

3:36

get. Less

3:50

is taken on radio podcast

3:52

that explores the hidden inside

3:55

of everything with your home

3:57

even dubner. When.

4:05

You look back to at the body

4:07

of research that you've done, some of

4:10

which brought you a Nobel prize. Congratulations

4:12

Thanks! How do you assess it's importance

4:14

or leverage in terms of influencing public

4:16

policy? And really, what I really want

4:19

to know is, is that a goal?

4:21

I suspect he'll say not, but maybe

4:23

if you're being a hundred and ten

4:25

percent honest, you might say, well, a

4:27

little bit sometimes. Well. I liked

4:29

to influence public policy and I'm pat me when

4:32

I influence public policy, but that is not what

4:34

I get up in the morning and set out

4:36

to do. I'm. An academic M.

4:38

What I set out to do

4:41

is I quality scholarship. I.

4:43

Like to get things published in top

4:45

journals. That's how I measure my influence.

4:47

Now I'm ultimately are a lot of

4:49

the work I do does affect public

4:51

policy, or at least it becomes part

4:53

of a discussion and that's gratifying. This

4:57

is Josh angriest than I'm a

4:59

professor in the Department of Economics

5:01

at Mit. He did win a

5:03

Nobel long with David Card and

5:05

Hijo in Benz for their quote

5:08

methodological contributions to the analysis of

5:10

causal relationships. That's a fancy way

5:12

of saying that. These three economists

5:14

have found reliable ways to

5:16

measure if a given factor

5:18

with all at x is

5:20

the actual cause of a

5:22

given outcome. Let's call it

5:24

Why for the something other

5:27

than x cause, why. We're.

5:29

Does x perhaps cause. Z.

5:32

Which may be the opposite of why. He

5:35

if you are a policymaker hoping

5:37

that X policy will cause why.

5:40

But. It causes Z

5:42

instead. well, good intentions

5:44

but not so good

5:46

outcome. This gap between

5:48

policy intention and policy

5:50

outcome is something that

5:52

Josh Angriest is particularly

5:54

interested in. Consider Us

5:56

tax policy. We. don't tax

5:58

the rich it on or percent But

6:00

sometimes we've been taxing poor people at

6:03

100 percent or even more because there's what's

6:05

called a cliff where

6:07

when you cross a threshold you

6:10

lose an entire benefit. The

6:12

classic example of a cliff in

6:14

social policy is you lose your

6:16

Medicare. If you earn more than

6:19

a cut off, Medicare

6:21

is worth tens of thousands of dollars to your

6:23

family and once your earnings

6:25

go one dollar above something you

6:27

lose Medicare. For the incentives they

6:29

are very poor for employment. You

6:32

don't want to change jobs and you don't want to move.

6:34

So all of a sudden there's a lot of trade offs.

6:36

Here's the thing. Making good social

6:39

policy is hard for a lot

6:41

of reasons. First it's

6:43

difficult to know for sure what works, whether

6:46

X will actually cause Y.

6:49

Second, policy making is

6:51

part of politics and politics

6:53

is messy with all kinds of compromises

6:56

to be made along the way. But

6:59

perhaps the trickiest thing is that

7:01

the people who are targeted by

7:03

a given policy may react in

7:05

a way the policymakers had not

7:08

anticipated. In a way that

7:10

may cause the policy to essentially

7:12

backfire, at least to some degree. This

7:15

has come to be known as the

7:17

law of unintended consequences. It's

7:20

not really a law but it

7:22

is a powerful and usually unwelcome

7:24

force. Josh Angrist has

7:27

been thinking about unintended consequences for

7:29

pretty much his entire career. His

7:32

first big research finding along with

7:34

the economist Daron Asimovlu had

7:36

to do with the ADA or

7:38

the Americans with Disabilities Act. It

7:41

was signed into law in 1990 by

7:43

President George Bush I. The

7:46

Americans with Disabilities Act expanded

7:48

civil rights protection, meaning

7:50

you could not fire or refuse

7:52

to hire or pay less

7:55

on the basis of a disability.

7:58

And when disabilities were were included in the

8:00

first version of the ADA. Well, it's

8:03

not concrete. That was one

8:05

of the things that had to get figured out. There

8:07

was a lot of litigation about what could

8:09

be counted. It's tricky because maybe the employer

8:11

doesn't know you're disabled. So there's always a

8:14

lot of litigation in the US. When

8:16

a new policy comes in, the courts decide. Ultimately,

8:20

the courts gave a fairly broad

8:22

interpretation. So it covers a wide

8:24

range of physical disabilities, including

8:27

some that might not be obvious to an employer,

8:29

like back pain. The other

8:31

thing is, and this was relatively novel,

8:34

the ADA requires employers

8:36

to accommodate disabled

8:38

workers. It's not clear

8:40

what that means. The law says it has

8:42

to be reasonable. So

8:44

for example, if you're a construction worker

8:47

building skyscrapers and you're in a wheelchair,

8:50

I don't have to accommodate that you can work on the

8:52

110th floor. But

8:55

if you work at MIT, I do have

8:57

to accommodate that you can get into your

8:59

space and do your work. And here

9:01

is how President Bush put it at the time. With

9:04

today's signing of the Landmark

9:06

Americans for Disabilities Act, every

9:09

man, woman, and child with

9:11

a disability can now pass through once

9:14

closed doors into a

9:16

bright new era of equality, independence,

9:18

and freedom. That sounds

9:20

pretty great, doesn't it? At least

9:23

from the employee side. It

9:25

might make things more complicated and

9:27

expensive from the employer's side,

9:30

but hey, there are trade-offs everywhere,

9:32

right? For a society

9:34

intent on providing good employment opportunities

9:36

for everyone, the ADA seemed to

9:38

say that it was worthwhile to

9:40

ask employers to make these accommodations.

9:43

Josh Angrist, meanwhile, he

9:46

got to wondering, would there be

9:48

some unintended consequences of this law

9:51

with such obviously good intentions? So

9:54

he went looking for some data, and

9:56

he found it in what's called the

9:58

Current Population Survey, every

10:00

month by the Census Bureau. That's

10:02

where the unemployment rate comes from. You know,

10:04

every month you hear the unemployment rate on

10:06

the radio, that's coming from a survey of

10:08

60,000 households. And there's

10:11

a bunch of questions there. Did you work? Were you

10:13

looking for work? But there's

10:15

actually a lot more there. There's employment,

10:17

there's earnings, there's schooling. And

10:19

as it turns out, there's a question about disability.

10:22

Do you have a disability that limits work? For

10:24

people who answered that question, yes. Angrist

10:27

wanted to know whether the ADA was helping. But

10:31

he would need some kind of control

10:33

variable, a way to compare workers affected

10:35

by the new law with

10:37

similar workers who weren't affected. Luckily,

10:41

the ADA provided one. Companies

10:44

with fewer than 25 employees were

10:46

exempted from most of the law's provisions.

10:50

This gave Angrist and Asamoglu a

10:52

nice little natural experiment, as

10:54

economists like to call it. So, they

10:57

measured, they sorted, they analyzed, and

11:00

they wrote up their findings for the Journal

11:02

of Political Economy, one of the top journals

11:04

in their field. What

11:06

was the headline result? The surprising

11:08

unintended consequence is that the ADA does

11:10

not seem to have helped disabled workers.

11:13

If anything, it made them worse off by

11:15

reducing their employment rates than

11:18

their annual earnings. And can you

11:20

explain why their employment would have been reduced? What's

11:23

actually happening at a firm? Well, employers,

11:25

to the extent that they can tell who's disabled, they

11:28

just don't want to get into it, because they

11:30

don't know what the cost of accommodation are going

11:32

to be. The cost of

11:34

accommodation could be very mild. It

11:37

could just be a matter of letting somebody work on the

11:39

ground floor, say. But they

11:41

could be very high, and they're sort of

11:43

unknown. Once you go down that

11:45

road and you're in the business of accommodating,

11:48

you potentially are on the hook for something big. Now,

11:50

mostly that's not going to happen, but it is a problem.

11:54

Now, wait a minute. Wasn't part of

11:56

the Americans with Disabilities Act a provision

11:58

that a firm wouldn't do? discriminate

12:00

against hiring a disabled worker, and therefore

12:02

by choosing not to hire a disabled

12:05

worker because you didn't want to deal

12:07

with the consequences, you are violating the

12:09

ADA. But it's much harder to make

12:12

a case on the hiring front than

12:15

on the discharge. How

12:17

do you make a case that I'm not hiring? You don't

12:19

have any data on who I interviewed, and it's

12:22

much harder to prove that, because I

12:24

can always say, well, that guy wasn't

12:26

qualified. We don't know if Josh Angris

12:28

was our first choice for this interview,

12:30

for instance. Totally, probably not. So

12:32

some workers you might decide, even if you don't

12:35

know they're disabled, you can kind of predict this

12:37

person is going to be trouble, and

12:39

they're going to sue me. And that

12:41

becomes much easier once they're in the workplace. And

12:44

the thing is, you don't have to win for this to be a problem.

12:47

Mostly you won't win. It'll settle. But

12:49

it's a hassle, and it's a cost. When

12:52

I first read this paper years ago, it

12:54

was one of those mind-blowing but obvious in

12:57

retrospect findings. Like, if you hadn't spelled it

12:59

out, I wouldn't have thought of it on

13:01

my own. But once you spell it out,

13:03

you see, yes, that's the way humans behave.

13:05

Well, it wasn't obvious at the time, and

13:08

it certainly was controversial. You

13:10

must have upset some people? Yeah. Yeah,

13:12

there were people that weren't happy, some

13:14

disabled groups that were proud of the

13:16

ADA. There were some economists

13:19

that didn't accept the finding and went and

13:21

did their own work on it. But,

13:23

you know, the finding mostly held up in my

13:25

view. We also did some

13:27

cross-state analysis. Some states have more

13:30

litigation than others. And we saw

13:32

that that was a good predictor of where

13:34

relative employment of disabled workers is going to

13:36

fall. The

13:39

ADA has been amended since its

13:41

original passage. It also

13:43

had its scope narrowed by several Supreme

13:46

Court cases, while some states have passed

13:48

their own laws to protect workers with

13:50

disabilities. That said,

13:52

such workers are still far less

13:54

likely to be employed than workers

13:57

without a disability. Meanwhile,

14:00

continue to study which policies

14:02

are best at actually helping

14:04

workers with disabilities, that after

14:06

all is the economist's job,

14:08

to analyze the costs along

14:10

with the benefits. I mean,

14:12

my job is to just point out the trade off. That

14:17

is Zoe Cullen. She too is an

14:19

economist at the Harvard Business School. Her

14:22

students are in training to create

14:24

and manage organizations. I like

14:26

the study of organizations. I think that's

14:29

where my topics, which would maybe

14:31

traditionally be more on the labor

14:33

end of economics, become

14:35

managerially relevant. For

14:37

instance, how do firms

14:39

and managers set salaries for

14:42

their employees? Do all

14:44

employees with the same job title and

14:46

experience get paid the same? And

14:49

how much do employees know

14:51

about their colleagues' salaries? For

14:55

employees, this is an important question if you're

14:57

looking for a job or if you're hoping

14:59

for a promotion. You

15:01

find out that you're up for promotion and

15:03

you're going to negotiate this new contract. And

15:06

the first thing you wonder is, well, what

15:09

are they willing to pay me? And

15:11

if you could only just talk to

15:13

the people who recently were promoted, recently

15:15

negotiated their contracts, maybe you could hold

15:18

out for a better deal. Most

15:20

firms are not very explicit about the

15:22

salaries they pay. Sometimes they'll give

15:24

a range. And there are

15:27

sites like Glassdoor that compile data

15:29

from former and current employees. But

15:31

this kind of data isn't complete

15:33

or even all that reliable. It's

15:36

posted anonymously. It includes

15:38

a lot of lower paid and

15:40

often disgruntled employees. The

15:42

data can also be outdated and failed

15:44

to include total compensation, like benefits. From

15:48

the other direction, some companies have

15:50

been found to solicit fake positive

15:52

feedback on Glassdoor. So

15:54

if you look at the whole picture, the

15:57

available data around salaries

15:59

is... often imperfect information.

16:03

Most of the theories put forth by

16:05

labor economists, meanwhile, assume

16:07

something closer to perfect

16:09

information. Yeah, so for example, if

16:12

an employer has perfect information

16:14

about market prices, they're going

16:16

to indeed know everything they

16:18

need to know about

16:21

market prices to make their decision. When

16:24

we introduce incomplete information, that's

16:26

typically a model where we

16:28

have to be more explicit

16:30

about exactly what's outside

16:32

their information set. So

16:34

in the case of market

16:36

wages, the employer might have

16:38

private information about exactly

16:41

what they think a candidate is worth and what

16:43

they would be willing to pay, but they

16:46

only have either a signal or know

16:48

the distribution of pay that

16:51

their competitors are drawing from. An

16:53

employer does have the obvious advantage of at

16:55

least knowing what they pay all of their

16:58

employees. Employees, meanwhile, have

17:00

much less information. This

17:02

gives the employer some real leverage, and

17:04

some firms exploit that leverage when they

17:07

can. It happened to Josh

17:09

Angrist when he landed his current job

17:11

at MIT. I came

17:13

to MIT as a full-time faculty

17:15

member in 1996. I was

17:18

happy as a clam to be at MIT, thinking

17:20

I've really done well. And

17:22

my former thesis advisor, Orly Aschafoetter,

17:24

who is a very famous labor

17:26

economist, came to my office

17:29

to chat, and Orly said, so what

17:31

do you make? And

17:33

I said, I make $85,000. I can't believe I make $85,000 a year. And

17:35

he said, oh

17:41

my God. He said, you can't work

17:43

for that. That's not what

17:45

tenured labor economists make. If that

17:48

gets out, that's going to be very bad. So

17:50

you need to go and get a

17:52

raise. Orly was teaching at Princeton at

17:54

this time? Yes. He's inciting you to

17:56

riot against MIT, basically. Exactly. I didn't

17:59

know what my... Years make. As

18:01

somebody has written, you're more likely to

18:03

know about your colleagues' sex lives than

18:05

their salaries, and I now know having

18:08

served on some committees that the variance

18:10

within departments can be huge. So

18:12

I was absolutely on the low end. so

18:14

would you do? I went and

18:17

I said i'm gonna leaves and I had

18:19

to generate. You know what academics have to

18:21

do is generate offers if you want threatened

18:23

to leave. It's like baseball.

18:25

You have to say, I'm going to get myself

18:28

traded. To the Yankees, could you

18:30

get an offer from Princeton for instance, for

18:32

instance, And then you

18:34

have to be. You know you have. It has to be

18:36

credible Cf to be prepared to take it if they. Is

18:38

no down pony up so you did that.

18:40

I did that and eventually I got a

18:43

race. Would you get a raise to? Lot.

18:45

I I gotta Ray I got a very

18:48

large raise. I think I got up to

18:50

around one sixty which shows you how I

18:52

was very underpaid. Would cut of that raise.

18:54

Did you give the early? I still owe

18:56

him. The thing is I owe early for

18:58

so many famous at that wouldn't begin to

19:00

pay. What does that say to you? The

19:03

very fact that Gap exists between the Eighty

19:05

Five and the Ones Fifty and all you

19:07

had to do was basically make it transparent.

19:09

What does that tell you about the way

19:11

firms operate? Mit is just another from well

19:13

as some labor markets are more. Efficient than

19:16

others. So we live in a labor

19:18

market. Were meeting professors. There's a lot

19:20

of variance in pay, and there's us

19:23

a lot of variance in productivity. Were.

19:26

Much closer to. People.

19:28

In the performing arts or

19:30

sports, Every actor or entertainer,

19:32

Every Mlb, and Ba, Nfl,

19:34

you name it. They're all

19:37

their own market, right? They

19:39

all have their own package

19:41

of attributes and it is

19:43

kind of hard to know.

19:46

What's. The they be paid. So

19:49

what would happen if from decided

19:51

to be more transparent about what

19:53

all their employees were paid? Wouldn't

19:55

that be a net benefit for

19:58

employees? may people

20:00

sick. Back to the break,

20:02

the law of unintended consequences strikes again.

20:05

I'm Stephen Dubner. This is Freakonomics Radio. We'll

20:07

be right back. Freakonomics

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Learn more at wellsfargo.com. You

21:17

may have noticed that there is a

21:19

growing trend in what are called pay

21:21

transparency laws, which require firms to give

21:24

more complete information about what employees should

21:26

expect to earn. A number

21:28

of countries have passed such laws recently

21:30

and it's catching on in the US

21:33

too, in places including California and New

21:35

York. The Harvard Business

21:37

School economist Zoe Cullen has been

21:39

studying the impact of these laws.

21:42

What are the desired outcomes of

21:44

such policies? And are those outcomes

21:46

achieved? Are these policies

21:48

fair? Cullen

21:50

says that pay transparency policies

21:52

come in three categories. The

21:54

first establishes what she calls

21:56

horizontal transparency. It's just a

21:58

simple way of saying I have a

22:00

peer, what is the pay transparency between these two

22:03

peers? The second category

22:05

is called cross firm transparency.

22:08

This is where the visibility is

22:10

now between firms, so you'd be

22:12

learning about what you'd get if you moved.

22:14

This is, let's say, the Yale School

22:16

of Management comes after Zoe Cullen from

22:19

Harvard Business School, correct? Correct. Has

22:21

that happened, by the way, for the record? I'm

22:24

not allowed to publicly talk about that, am I? It's

22:28

not only transparency that a worker

22:30

might have about different firm salaries,

22:32

but it's also actually what a

22:34

firm might perceive their competitors to

22:36

be paying. The third

22:39

category is what Cullen calls

22:41

vertical transparency. Vertical

22:43

is this understudied, very important midpoint. There

22:45

are very few instances where public policy

22:47

is really focused on vertical transparency, but

22:49

this is happening to some extent by

22:52

accident, which is finding out not just

22:54

what your peers are earning, but what

22:56

your senior management is earning, what you

22:59

would earn if you were to go

23:01

up the corporate ladder. Why

23:04

would you say public policy hasn't yet

23:06

cared much about vertical? Because I would

23:08

think, given all the

23:10

conversation about pay inequality and income inequality over

23:12

the past many years now, that that would

23:15

be actually a focus, even

23:17

more than horizontal, perhaps.

23:20

My sense is that most of us

23:22

think firms are taking care of this

23:24

in the way that they are doing

23:26

in the economic textbooks. In an economic

23:28

textbook, you might say, well, the firm

23:30

has to internalize the career incentives of

23:32

the employee, and they should incentivize

23:34

them to both stay with the firm and

23:37

see that they are growing into bigger positions

23:39

in order to keep them loyal to the

23:41

firm. So the idea would be that it's

23:44

the onus of the firm to essentially be

23:46

very clear about the steep salary progression. Do

23:49

younger employees tend to talk about

23:51

pay transparency and want to have

23:53

more pay transparency than

23:55

older workers? But

24:00

let me just say this, we also see

24:02

a strong pattern of secrecy

24:04

as you become richer, and age

24:07

and wealth are highly collinear. Tom

24:10

Nicholas and I, my colleague at HBS,

24:12

looked at who withholds their information about

24:14

income from the US Census, and

24:17

there the patterns seem pretty strong that

24:19

it is about the money. Now,

24:22

you must have some psychologist friends

24:24

put on your psychologist hat for

24:27

a moment and try to explain

24:29

why that would be. The

24:31

term that comes up most often when

24:34

you just have an open text box

24:36

for why don't you want

24:38

to share this information is around

24:41

fear of resentment. So

24:43

that word resentment, I take quite seriously,

24:46

you might also think there's some strategic

24:48

element to this that is harder to

24:51

articulate. So in the context of a workplace, you might

24:53

have a sense that if it got out that you

24:55

just got a big raise, perhaps other people would be

24:57

vying for the same pie. Vying

25:01

for the same pie, meaning if your

25:03

salary goes up, there may not be

25:06

as much pie left for me. So

25:09

what happens once salaries are made

25:11

public? That's the

25:13

question Cullen set out to answer.

25:15

She recently published a paper that

25:17

surveyed all the relevant research on

25:19

paid transparency policies, including her own

25:21

research. This was for

25:23

the Journal of Economic Perspectives. Most

25:26

economics journals want their contributors

25:28

to describe the research but

25:30

remain agnostic about policy recommendations.

25:33

But the JEP is different. They

25:35

like us to take a stance. Hence

25:38

the title is paid transparency, good. So

25:41

less of a literature review and much

25:43

more about where I stand on this

25:45

topic. And the implication there

25:48

would seem to be that more information

25:50

is good, full stop. Is that the

25:52

case? That's what I think

25:54

people think. And you're here to tell us...

25:57

Just that it's, you know, the typical economist,

25:59

not... So simple view. Cullen's

26:03

own research used data from a variety of

26:05

sources. Some came from TaskRabbit,

26:08

the online platform where gig workers

26:10

submit public bids for different jobs.

26:13

Some data came from ADP, the

26:15

huge payroll company that manages worker

26:17

paychecks and benefits across the country.

26:20

She also relied on census data that

26:22

tracked wages and employment histories for more

26:24

than 5 million people. This

26:27

allowed Cullen to compare workers in

26:29

states that have pay transparency regulations

26:31

with those that don't. Here's what she found.

26:34

At companies in states that have

26:36

pay transparency laws, wages

26:38

became, as she puts it, linked

26:41

together. When one person

26:43

negotiates for their salary, typically it will

26:45

be in an environment that's highly private.

26:48

Once you start to introduce transparency as a

26:50

way of pinning the employer to pay people

26:52

the same, suddenly one

26:55

person's negotiation affects someone else's.

26:57

Okay, so that makes sense. When

27:00

salary information is private, there

27:02

might be a lot of variance. Once

27:04

it's public, wages tend to

27:06

converge. And that's

27:08

exactly what these laws are

27:10

trying to accomplish to prevent

27:12

firms from rewarding or punishing

27:15

individual employees based on

27:17

some kind of bias or favoritism.

27:20

But that wasn't the only way these

27:22

laws affected firms. You can

27:24

just see how it increases their incentives to

27:26

bargain aggressively, because if they save a dollar

27:28

with one worker, they save that dollar with

27:30

everybody. Ultimately, you start to

27:32

see what I call compressed pay. In

27:35

one paper, Cullen finds that

27:37

in states with transparency laws

27:39

that protect workers' rights to

27:41

discuss their compensation, there

27:43

was, overall, a 2% decline in wages.

27:48

Essentially, the set of pay transparency policies

27:50

that have been most popular are also

27:53

having this unintended consequence of

27:56

Linking bargaining practices across workers

27:58

and lowering average. The Wages.

28:01

So. Does your finding Zoe

28:03

that pay transparency can lead

28:05

to overall lower pay? Does

28:07

it strike you as an

28:09

unintended consequence of the policies?

28:12

I don't think policymakers talked about that

28:14

component of it. So that the yes,

28:17

Yea. I think I didn't know it myself

28:19

and I found it very surprising. Can

28:21

you give us a sense

28:23

of the impact of pay

28:26

transparency on any observable pay

28:28

gap between women and men?

28:30

I assume that is a

28:32

major goal of pay transparency.

28:35

I think it's pretty uniformly

28:37

great at A the School

28:39

in all these evaluations as

28:42

country policy as we've seen

28:44

Positive: A on gender gap

28:46

p Transparency. Leads to more equal

28:48

pay. Is. It to reductive

28:51

to say then that paid

28:53

transparencies on average significantly better

28:55

for women than men. Know

28:58

I think that it's a challenging statement to

29:00

make in part because well, I think the

29:02

presumption that you have in the back of

29:05

your mind is that women at the lower

29:07

end of the pay scale and so this

29:09

is a question of to the people at

29:11

the bottom necessarily do better under P. Transparency:

29:13

When we talk that societal level transparency, I

29:16

don't think it's clear. So. Sounds

29:18

like you might consider this a

29:20

sort of growing pains of pay

29:22

equity. Would that be accurate? Yes,

29:24

And I can't emphasize enough that.

29:27

With slight adjustments to what these policies

29:29

are doing, in fact, you can get

29:31

paid gross and upward pressure on pay

29:34

through transparencies. It's as though you can

29:36

get your cake and eat it too.

29:38

With just sinking a little more broadly

29:40

about the intended goal. Can. I

29:42

have those slight adjustments please? Yes!

29:45

Like you know, don't focus so I

29:47

say don't amusing the imperative with you.

29:49

A success massacre. That means you're comfortable.

29:52

I'm happy about that. He

29:54

okay, thank you Steve and I think that's an invitation.

29:57

If it's been a madame I hereby from.

30:00

invite you to use the imperative. So

30:02

don't focus so much on

30:04

trying to get one employee

30:07

to compare themselves to another

30:09

employee. We want the

30:11

comparisons to be one firm

30:13

realizing that in order to compete

30:16

for talent they need to raise wages.

30:18

Firms have to understand what the competing wages look

30:21

like and employees have to figure out where to

30:23

send those applications. The

30:25

idea here is really important. You

30:27

have a setting where employers don't

30:29

typically have full information about market

30:32

pay, workers don't have full information

30:34

about market pay. With simple

30:37

information tools you

30:39

can see that employees respond

30:42

by submitting applications not only

30:44

outside of their own occupation but

30:46

to higher paying firms within their occupation.

30:49

When you start to increase what

30:51

they know about the rest of the

30:53

market it's exactly the people who are

30:55

being underpaid that are positively surprised.

30:59

There will likely be more pay transparency

31:01

laws in the coming years. The

31:04

Biden administration just announced it

31:06

plans to require federal contractors

31:08

to provide job applicants with

31:10

expected salary ranges. I

31:13

know there are quite a few Freeconomics Radio

31:15

listeners in the White House. If you want

31:17

to give Zoe Cullen a call to help

31:19

design that policy we'd happily pass on her

31:21

number. I do wonder if she

31:23

will use the imperative with you. Coming

31:26

up after the break one more

31:28

instance of good intentions and

31:31

an unintended consequence. It's definitely

31:33

a frustrating finding because it shouldn't

31:36

be a trade-off. I'm Stephen Dubner.

31:38

This is Freeconomics Radio. We'll be right back.

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wherever you get your podcasts. You can

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listen ad-free on the Amazon Music or

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Wondery app. Over

34:21

the past few decades, a lot of

34:23

policies have been designed to make workplaces

34:25

more equitable for women. But

34:27

as we've been hearing, not

34:29

all policies achieve their desired

34:31

result. Consider the research

34:34

done by the economists Peter Blair and

34:36

Ben Pozmanik. They found that

34:38

family leave policies, which give employees

34:40

time off for childbirth and other

34:42

family issues, have had the

34:44

unintended consequence of increasing the average

34:46

pay gap between men and women.

34:50

Why? Because women are more likely

34:52

to take advantage of these policies and

34:54

men end up earning

34:56

more on average. So

34:58

as we've been hearing throughout

35:00

this episode, good intentions do

35:03

not guarantee good outcomes. Consider

35:06

the recent work of Marina Gertzberg.

35:08

She was born in Ukraine, grew up

35:11

in Germany, got her PhD in the

35:13

Netherlands, worked for a time in New

35:15

York and now teaches in Australia. I'm

35:17

an assistant professor at the University of Melbourne

35:19

in the finance department. Before

35:22

getting her PhD, Gertzberg had a

35:24

variety of internships and jobs in

35:27

a variety of industries. Banking, management

35:29

consulting, the art industry. I

35:32

worked my whole life in industries

35:34

that are male dominated. And I

35:37

have to say that I felt fairly comfortable

35:39

as a woman. I didn't notice that much

35:41

discrimination or anything like that. More

35:44

recently, Gertzberg got to wondering

35:46

about discrimination in academia, specifically

35:49

in academic research. She

35:52

decided to focus on research

35:54

collaborations. This is an especially

35:57

important area for younger academics.

36:00

Working to the senior person that has

36:02

a lot of advantages to sing and

36:04

person have a network promoting the paper.

36:06

It's easier potentially the name of To

36:09

Sing Academic also, health concerns, submissions also

36:11

and publication process. The name of the

36:13

senior present will help. Of course the

36:16

knowledge of the senior person is also

36:18

very helpful. They usually have more experience

36:20

with how to frame the paper what

36:23

as a convincing methodology? What is. An

36:25

interesting question: collaborations are

36:27

really important for productivity

36:29

and academia. But collaboration

36:32

is in academia or different from

36:34

collaboration in many professional settings. In

36:36

most places, a junior person is

36:38

assigned to senior person or gets

36:41

attached to a project that a

36:43

senior person is running. Part.

36:45

Of our profession as that our collaboration.

36:47

See might have Ghana cleats We decide

36:49

l self. Will be work with. So if

36:51

you don't want to work with a person,

36:53

you just won't work on a project with

36:55

them. No one can foresee. On what

36:58

is also really important aspect

37:00

is that the lines between

37:02

the professional and personal Oftentimes

37:05

blur so we create a

37:07

D S. Outside of the

37:09

authors who work on of

37:12

the efforts, we discuss ideas

37:14

in informal setting such as

37:17

interface over dinner and be

37:19

a context where ambiguous situations

37:21

could arise. Ambiguous

37:23

situations Like is this

37:26

collaboration purely professional. Or.

37:29

Is there perhaps a romantic

37:31

or sexual component? Given

37:34

the realities and history of

37:36

the male female dynamic. Hertzberg

37:39

thought about cases in which

37:41

a junior female academics collaborated

37:43

with a senior mail academic.

37:46

And she started to put together a research project.

37:49

much like the joss angriest research we

37:52

heard about earlier that had a fulcrum

37:54

events in that case the passage of

37:56

the a d h b americans with

37:58

disabilities act goods bird research would also

38:01

have a fulcrum, a before and an

38:03

after. In this case, the before and

38:05

after was the Me Too movement. So,

38:08

people view the event date of the

38:10

Me Too movement as October

38:12

15, 2017, when Elissa Milano

38:15

tweeted that she was sexually harassed

38:18

and encouraged other women to come forward

38:20

as well. The Me Too

38:22

movement was meant to expose men

38:24

who had sexually harassed women and

38:26

to prevent future harassment. Those

38:29

were the intended consequences. Gertzberg

38:31

wondered if there might be an unintended

38:34

consequence as well. Women

38:36

and men equally started to

38:38

express the perception that,

38:40

yeah, men may be taking now

38:42

precautionary action in interacting

38:45

with women, starting to be more

38:47

careful because they are concerned they

38:49

would be accused of sexual harassment.

38:52

And, yeah, this was the time when the idea was

38:55

actually born. The idea

38:57

being a research paper, which she

38:59

would eventually call the unintended consequences

39:01

of Me Too, evidence

39:03

from research collaborations. At the

39:06

time, I also pitched it to a

39:08

senior academic to just get a sense

39:10

whether this was a good idea. The

39:12

senior academic told me that I should

39:14

rather not work on this at this

39:16

stage in my career. Why not?

39:19

Well, because potentially it would be

39:22

too controversial. Was this

39:24

a male or female academic? It

39:26

was a male academic. And yet you

39:28

ignored this person's advice plainly? No,

39:30

I didn't ignore it. I didn't work on it

39:33

for some time because that was during my PhD

39:35

and I had other things on my plate as

39:37

well. So I decided to not work on it. But

39:41

Gertzberg couldn't get the idea out of her head. Once

39:45

she got settled into her first academic

39:47

job in Australia, she put

39:49

together a hypothesis. The interesting

39:51

thing about the Me Too movement

39:53

is that Its

39:55

purpose is to increase protection

39:57

for women from sexual abuse.

40:00

Harassment. So technically women should

40:02

feel very comfortable or more

40:04

comfortable to work. but man,

40:06

after the movement. on the

40:08

other hand, there's a lot

40:10

of anecdotal evidence and over

40:12

savvy evidence that men are

40:14

concerned about sexual harassment accusations.

40:17

After the me too movement. So

40:19

it is unclear what effect the

40:22

movement would have on collaborations between

40:24

woman and man. So it is

40:26

a two sided hypothesis. Tude.

40:28

Tests her hypothesis groups. Berg began

40:31

collecting data on junior female economists

40:33

women who had recently gotten their

40:35

Ph and were hired into university

40:38

economics departments on a tenure tracked

40:40

shortly before the apex of me

40:42

to there were not all that

40:45

many women in her sample fewer

40:47

than one hundred. C. Gather

40:49

data on the research papers they

40:51

were publishing and who they were

40:54

collaborating with. You want to know

40:56

what happened to collaboration between those

40:58

junior female academics and their male

41:00

colleagues? and which he find. I

41:03

find that junior seem like they're

41:05

mixed that fewer new projects after

41:08

me too, and that is mainly

41:10

due to see a collaboration with

41:12

mail authors before the Me Too

41:14

movement on efforts sunni a woman

41:17

would start one point six new.

41:19

Projects for year. And

41:21

after the me to move men's women's.

41:23

Thought and effort there. a point nine you

41:25

projects. For. Yes, Ah, the magnitude of Zero

41:28

Point Seven. Projects Q after them

41:30

into movement, which is about forty

41:32

four percent. Oh my goodness, and.

41:34

Sixty percent of that decline. I'd

41:37

you to see a color. Patience with

41:39

mail cost us. I mean,

41:41

that is a massive drop. How do

41:43

think about the size of the harm

41:45

to the career of a junior female

41:47

academic based on that number. We.

41:50

Already know that woman has

41:52

less output and man, and

41:54

that partially explains forestry outcomes

41:57

for woman. and it could

41:59

be. If such as lower

42:01

tenure rates, so having this product

42:04

of output is crucial and if

42:06

that declines that could widen the

42:08

gender gap between women and men

42:11

in academia. Of course it

42:13

is also important to them

42:15

look at the outcomes. for

42:17

example tenure rates of woman

42:19

after me to or publication of

42:21

pounds and that is also something

42:24

that I'm setting. One challenge is

42:26

that had a publication processes very

42:28

very long and. For lot

42:30

of the projects that started.

42:33

Off to meet you. There are no outcomes

42:35

yet. What? Can you tell

42:37

us about the degree to which junior

42:39

women sought out senior women to collaborate

42:41

with? Us to me to

42:44

women do not increase. Collaboration with

42:46

any types of women and why

42:48

do you think that is? It.

42:51

Be for example that woman may

42:53

need more time to adjust and

42:55

find new collaborator among women. It

42:57

to the also be the case

42:59

that there's simply not enough for

43:02

months for women to substitute with.

43:04

So if they think about the

43:06

numbers even among junior seem like

43:08

a damn. Makes their about thirty

43:10

percent female. So even substituting with

43:13

new junior woman is sally to

43:15

settle for those numbers. So.

43:17

You're saying that among senior

43:19

female researchers, there's only like

43:21

sixteen percent, exactly. So you're

43:24

see put, a sixteen percent

43:26

are probably oversubscribed with junior

43:28

collaborators are, and there just

43:30

isn't enough availability for junior

43:32

women to make new collaboration

43:35

with them. Yes, Yeah, that's

43:37

the one explanation. for

43:42

what it's worth marina birds birds

43:44

had no collaborators on her paper

43:46

about this unintended consequence of the

43:48

me too movement it's worth noting

43:50

that her sample size was small

43:52

and her timeframe relatively tight so

43:54

we should be cautious in giving

43:56

her findings too much weight but

43:59

a group of researchers at the

44:01

University of Cambridge seem to have

44:03

corroborated Goertzberg's results using different methods.

44:06

And if you go outside the

44:08

academic world, you see a similar

44:10

effect. A recent Pew poll shows

44:12

that nearly 50% of men say

44:14

it's harder for them to know how

44:16

to interact with women at work. And

44:19

here is how a headline from Bloomberg News put

44:21

it, Wall Street rule for

44:23

the Me Too era, avoid

44:26

women at all costs. Here's

44:29

Marina Goertzberg again talking about her

44:31

own research. It's definitely a

44:33

frustrating finding because it shouldn't be

44:35

a trade-off. Women shouldn't have to

44:37

choose between a safe workplace, not

44:39

being sexually harassed, and their true

44:41

outcomes of productivity. On the

44:43

other hand, I think my finding suggests that we

44:46

can do something about that. As

44:49

some time passes, there

44:51

will be a new equilibrium and men and

44:53

women know how to interact with each other.

44:56

But it is also important to define

44:58

what the expectations are for behavior so

45:01

that men don't think, oh, I just have

45:03

to say the right thing and I'm going to get fired.

45:07

And so it justifies my behavior by not

45:09

working with women. And what kind

45:11

of feedback has Goertzberg gotten

45:13

since publishing these controversial findings?

45:16

I posted the first version of the paper in August 2022

45:18

on Twitter, and I received a lot of

45:22

reactions from Twitter. I believe

45:24

this was also when Josh Angrist became aware

45:27

of the paper. And

45:29

I received an email from him saying

45:31

that the paper was interesting and that

45:33

it would create a lot of controversy.

45:36

That was really a high point of my career at

45:38

the time. And here again

45:41

is Josh Angrist from MIT. I

45:43

mean, it has a little bit of

45:45

an ADA-like flavor. So you tried to protect

45:47

a group, in this

45:49

case women, mostly young women. And

45:52

maybe what you did is you made people think, you

45:54

know, what's in this for me? I might just get

45:56

in trouble. So better

45:59

for me to stay awake. away. So it's

46:01

a lot like the employer who's worried about

46:03

being sued by, you know,

46:06

not accommodating or discharging a

46:08

disabled worker and then having

46:11

to deal with litigation. I have to

46:13

say, it's so fascinating to hear you

46:15

talk about these constructs in a way

46:17

that is, you know, quite rational and

46:19

compelling and so on. It's also, however,

46:21

Josh, if you don't mind me saying

46:23

so, a little bit depressing because there

46:25

are all these well-intended

46:27

policies and people writing policies

46:29

trying to help other people.

46:32

And we find that not in

46:34

a anywhere near majority of cases, or

46:36

at least I gather not, but in not

46:38

a tiny fraction, there is a backfire effect

46:40

of this unintended consequence. So how

46:43

does one as a right-thinking human who

46:45

wants the best for people and wants,

46:47

you know, employers and employees to be

46:49

happy and well compensated and so on,

46:51

how do you think your

46:53

way around the big issue of

46:56

all these unintended consequences that promote

46:58

worse outcomes instead of better outcomes?

47:00

Well, first and foremost, I want

47:03

to draw your attention to the

47:05

trade-offs. Personally, I guess that's

47:07

why I'm an economist. You know, I don't

47:09

find the possibility of trade-offs depressing. I

47:12

find the possibility of trade-offs interesting. That's

47:15

what I study. You know,

47:17

I recognize that it's not a perfect world and

47:20

that policy design is always about trade-offs.

47:22

Yeah. Yeah. And there's still things that

47:24

are worth doing in spite of the

47:26

trade-offs, but I want you to look

47:28

at that in a clear way and

47:31

be aware of that. Consider

47:36

us aware. Thanks

47:38

to Josh Angris, Marina Gertzberg, and

47:40

Zoe Collin for their excellent

47:42

teaching today. I learned a lot. I hope

47:44

you did too. We will

47:47

be back next week with a new

47:49

episode of Freakonomics Radio. Until then, take

47:51

care of yourself and if you can,

47:53

someone else too. Freakonomics Radio

47:55

is produced by Stitcher and Renbud Radio.

47:58

You can find our entire archive. on

48:00

any podcast app, also at freakonomics.com,

48:02

where we publish transcripts and show

48:04

notes. This episode is

48:07

produced by Zach Lipinski. Our staff

48:09

also includes Alina Cullman, Augusta Chapman,

48:11

Eleanor Osborne, Elsa Hernandez, Gabriel Roth,

48:13

Greg Rippon, Jasmine Clinger, Jeremy Johnston,

48:15

Uli Kanfer, Lyric Boudich, Morgan Levy,

48:17

Neil Caruth, Rebecca Lee Douglas, and

48:19

Sarah Lilly. Our theme song is

48:22

Mr. Fortune by the Hitchhikers. Most

48:24

of the other music was composed

48:26

by Luis Guerra. As always,

48:28

thank you for listening. The

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